Adaptive Finite-Horizon Group Estimation for Networked Navigation Systems with Remote Sensing Complementary Observations under Mixed LOS/NLOS Environments

Abstract

Networked navigation system (NNS) enables a wealth of new applications where real-time estimation is essential. In this paper, an adaptive horizon estimator has been addressed to solve the navigational state estimation problem of NNS with the features of remote sensing complementary observations (RSOs) and mixed LOS/NLOS environments. In our approach, it is assumed that RSOs are the essential observations of the local processor but suffer from random transmission delay; a jump Markov system has been modeled with the switching parameters corresponding to LOS/NLOS errors. An adaptive finite-horizon group estimator (AFGE) has been proposed, where the horizon size can be adjusted in real time according to stochastic parameters and random delays. First, a delay-aware FIR (DFIR) estimator has been derived with observation reorganization and complementary fusion strategies. Second, an adaptive horizon group (AHG) policy has been proposed to manage the horizon size. The AFGE algorithm is thus realized by combining AHG policy and DFIR estimator. It is shown by a numerical example that the proposed AFGE has a more robust performance than the FIR estimator using constant optimal horizon size.